Abstract

Constructing ensemble classifiers that are both accurate and diverse is an important issue of research and challenging task in machine learning. In this paper, we proposed Class-based Random Subspace (CRS) method; a new ensemble construction method based on the random subspace (RS) strategy, and tested it on a number of standard data sets from UCI machine learning repository. Our results show that CRS is at least as good as RS, and outperforms it in datasets with strong correlation between their classes.

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